Готові списки джерел за темами / Generalized Autoregressive Conditional Heteroscedasticity

Добірка наукової літератури з теми "Generalized Autoregressive Conditional Heteroscedasticity"

Автор: Grafiati
Опубліковано: 19 червня 2021
Оновлено: 8 лютого 2022

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  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій

Статті в журналах з теми "Generalized Autoregressive Conditional Heteroscedasticity":

1

Rossetti, Nara, Marcelo Seido Nagano, and Jorge Luis Faria Meirelles. "A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries." Journal of Economics, Finance and Administrative Science 22, no. 42 (June 12, 2017): 99–128. http://dx.doi.org/10.1108/jefas-02-2017-0033.

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Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.
2

Xiao, Zhijie, and Roger Koenker. "Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models." Journal of the American Statistical Association 104, no. 488 (December 2009): 1696–712. http://dx.doi.org/10.1198/jasa.2009.tm09170.

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3

Zhang, Xibin, and Maxwell L. King. "Influence Diagnostics in Generalized Autoregressive Conditional Heteroscedasticity Processes." Journal of Business & Economic Statistics 23, no. 1 (January 2005): 118–29. http://dx.doi.org/10.1198/073500104000000217.

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4

Santi Singagerda, Faurani, Linda Septarina, and Anuar Sanusi. "The volatility model of the ASEAN Stock Indexes." Investment Management and Financial Innovations 16, no. 1 (March 18, 2019): 226–38. http://dx.doi.org/10.21511/imfi.16(1).2019.18.

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This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development. Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.
5

Jiang, Wen, Zheng Yan, Dong-Han Feng, and Zhi Hu. "Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model." European Transactions on Electrical Power 22, no. 5 (June 24, 2011): 662–73. http://dx.doi.org/10.1002/etep.596.

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6

Otto, Philipp, Wolfgang Schmid, and Robert Garthoff. "Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity." Spatial Statistics 26 (August 2018): 125–45. http://dx.doi.org/10.1016/j.spasta.2018.07.005.

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7

Bahramgiri, Mohsen, Shahabeddin Gharaati, and Iman Dolatabadi. "Modeling jumps in organization of petroleum exporting countries basket price using generalized autoregressive heteroscedasticity and conditional jump." Investment Management and Financial Innovations 13, no. 4 (December 29, 2016): 196–202. http://dx.doi.org/10.21511/imfi.13(4-1).2016.05.

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This paper uses autoregressive jump intensity (ARJI) model to show that the oil price has both GARCH and conditional jump component. In fact, the distribution of oil prices is not normal, and oil price returns have conditional heteroskedasticity. Here the authors compare constant jump intensity with the dynamic jump intensity and evidences demonstrate that oil price returns have dynamic jump intensity. Therefore, there is strong evidence of time varying jump intensity Generalized Autoregressive Heteroscedasticity (GARCH) behavior in the oil price returns. The findings have several implications: first, it shows that oil price is highly sensitive to news, and it does settle around a trend in long-run. Second, the model separates variances of high volatilities from smooth volatilities. Third, the model rejects an optimal path for extracting oil and technology transmission. In fact, the lack of a long-term pattern can cause excessive oil extracting which can result in heavy climatic effects. Keywords: generalized autoregressive heteroscedasticity (GARCH), jumps, basket, oil price, Organization of Petroleum Exporting Countries (OPEC), Autoregre-ssive jump intensity (ARJI). JEL Classification: C32, C52, F31
8

Haris, M. Al. "PERAMALAN HARGA EMAS DENGAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 3, no. 1 (July 22, 2020): 19. http://dx.doi.org/10.32493/jsmu.v3i1.5263.

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9

Yip, Iris W. H., and Mike K. P. So. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model." Mathematics and Computers in Simulation 80, no. 2 (October 2009): 327–40. http://dx.doi.org/10.1016/j.matcom.2009.07.001.

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10

Aminul Isl, Mohd. "Applying Generalized Autoregressive Conditional Heteroscedasticity Models to Model Univariate Volatility." Journal of Applied Sciences 14, no. 7 (March 15, 2014): 641–50. http://dx.doi.org/10.3923/jas.2014.641.650.

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Статті в журналах Дисертації

Дисертації з теми "Generalized Autoregressive Conditional Heteroscedasticity":

1

Widing, Härje. "Business analytics tools for data collection and analysis of COVID-19." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176514.

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The pandemic that struck the entire world 2020 caused by the SARS-CoV-2 (COVID-19) virus, will have an enormous interest for statistical and economical analytics for a long time. While the pandemic of 2020 is not the first that struck the entire world, it is the first pandemic in history where the data were gathered to this extent. Most countries have collected and shared its numbers of cases, tests and deaths related to the COVID-19 virus using different storage methods and different data types. Gaining quality data from the COVID-19 pandemic is a problem most countries had during the pandemic, since it is constantly changing not only for the current situation but also because past values have been altered when additional information has surfaced. The importance of having the latest data available for government officials to make an informed decision, leads to the usage of Business Intelligence tools and techniques for data gathering and aggregation being one way of solving the problem. One of the mostly used software to perform Business Intelligence is the Microsoft develop Power BI, designed to be a powerful visualizing and analysing tool, that could gather all data related to the COVID-19 pandemic into one application. The pandemic caused not only millions of deaths, but it also caused one of the largest drops on the stock market since the Great Recession of 2007. To determine if the deaths or other reasons directly caused the drop, the study modelled the volatility from index funds using Generalized Autoregressive Conditional Heteroscedasticity. One question often asked when talking of the COVID-19 virus, is how deadly the virus is. Analysing the effect the pandemic had on the mortality rate is one way of determining how the pandemic not only affected the mortality rate but also how deadly the virus is. The analysis of the mortality rate was preformed using Seasonal Artificial Neural Network. Forecasting deaths from the pandemic using the Seasonal Artificial Neural Network on the COVID-19 daily deaths data.
2

Odusami, Babatunde Olatunji. "A Study of Conditional Volatilities in Financial Markets using Generalized Conditional Heteroscedasticity Jump Models." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/1049.

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In this manuscript, I investigate the time-varying volatilities and co-volatilities in the fixed income and equities market using jump augmented stochastic volatility models. The results highlights that the fact that jumps are inherent in financial markets and have implications for the dynamics of volatilities and co-volatilities of financial assets over time. Jump augmented models provide a superior description of instantaneous market conditions and a promising avenue for future research in areas of asset pricing, portfolio selection, and risk management.
3

Oztek, Mehmet Fatih. "Modeling Co-movements Among Financial Markets: Applications Of Multivariate Autoregressive Conditional Heteroscedasticity With Smooth Transitions In Conditional Correlations." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615713/index.pdf.

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The main purpose of this thesis is to assess the potential of emerging stock markets and commodity markets in attracting the attention of international investors who utilize various portfolio diversification strategies to reduce the cumulative risk of their portfolio. A successful portfolio diversification strategy requires low correlation among financial markets. However, it is now well documented that the correlations among financial markets in developed countries are very high and hence the benefits of international portfolio diversification among these markets have been very limited. This fact suggests that investors should look for alternative markets whose correlations with developed markets are low (or even negative if possible) and which have high growth potentials. In this thesis, two emerging countries'
stock markets and two commodity markets are considered as alternative markets. Among emerging countries, Turkey and China are chosen due to their promising growth performance since the mid-2000s. As commodity markets, agricultural commodity and precious metal markets are selected because of the outstanding performance of the former and the "
safe harbor"
property of the latter. The structures and properties of dependence between these markets and stock markets in developed countries are examined by modeling the conditional correlation in the dynamic conditional correlation framework. The results reveal that upward trend hypothesis is valid for almost all correlations among market pairs and market volatility plays significant role in time varying structures of correlations.
4

Chang, Tsangyao. "An Application of Autoregressive Conditional Heteroskedasticity (Arch) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Modelling on Taiwan's Time-Series Data: Three Essays." DigitalCommons@USU, 1995. http://digitalcommons.usu.edu/etd/4040.

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In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980. In the first essay, analysis that accounts for structural change reveals that the fundamental relationship between inflation and its variability was severed by policies implemented during economic liberalization in Taiwan in the early 1980s. Furthermore, if residuals are corrected for serial correlation, evidence in favor of ARCH effects is weakened. In the second essay, dynamic linkages between daily stock returns and daily trading volume are explored. Both linear and nonlinear dependence are evaluated using Granger causality tests and GARCH modelling. Results suggest significant unidirectional Granger causality from stock returns to trading volume. In the third essay, comparative analysis of the frequency structure of the Taiwan stock index data is conducted using daily, weekly, and monthly data. Results demonstrate that the relationship between mean return and its conditional standard deviation is positive and significant only for high-frequency daily data.
5

Tinkl, Fabian [Verfasser], and Ingo [Akademischer Betreuer] Klein. "Asymptotic Theory for M-estimators in general autoregressive conditional heteroscedasticity models / Fabian Tinkl. Betreuer: Ingo Klein." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2013. http://d-nb.info/1077582838/34.

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6

Grego, Simone. "Modelos para relacionar variáveis de solos e área basal de espécies florestais em uma área de vegetação natural." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-03122014-142123/.

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O padrão espacial de ocorrência de atributos de espécies florestais, tal como a área basal das árvores, pode fornecer informações para o entendimento da estrutura da comunidade vegetal. Uma vez que fatores ambientais podem influenciar tanto o padrão espacial de ocorrência quanto os atributos das espécies em florestas nativas. Desse modo, investigar a relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica das florestas. Especificamente, neste trabalho, o objetivo é avaliar métodos estatísticos que permitam identificar quais atributos do solo são capazes de explicar a variação da área basal de cada espécie de árvore. A área basal foi considerada como variável resposta e como covariáveis, um grande número de atributos físicos e químicos do solo, medidos em uma malha de localizações cobrindo a área de estudo. Foram revisados e utilizados os métodos de regressão linear múltipla com método de seleção stepwise, modelos aditivos generalizados e árvores de regressão. Em uma segunda fase das análises, adicionou-se um efeito espacial aos modelos, com o intuito de verificar se havia ainda padrões na variabilidade, não capturados pelos modelos. Com isso, foram considerados os modelos autoregressivo simultâneo, condicional autoregressivo e geoestatístico. Dado o grande número de atributos do solo, as análises foram também conduzidas utilizando-se as covariáveis originais, fatores identificados em uma análise fatorial prévia dos atributos de solo. A seleção de modelos com melhor ajuste foi utilizada para identificar os atributos de solo relevantes, bem como a presença e melhor descrição de padrões espaciais. A área de estudo foi a Estação Ecológica de Assis, da Unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, Dinâmica e Conservação em Florestas do Estado de São Paulo: 40 ha de parcelas permanentes\", do programa Biota da FAPESP. As análises reportadas aqui se referem à área basal das espécies Copaifera langsdorffii, Vochysia tucanorum e Xylopia aromatica. Com os atributos de solo reduzidos e consistentemente associados à área basal, a declividade, altitude, saturação por alumínio e potássio mostraram-se relevantes para duas das espécies. Resultados obtidos mostraram a presença de um padrão na variabilidade, mesmo levando-se em consideração os efeitos das covariáveis, ou seja, os atributos do solo explicam parcialmente a variabilidade da área basal, mas existe um padrão que ocorre no espaço que não é capturado por essas covariáveis.
The spatial pattern of occurrenceis of forest species and their attributes, such as the basal area of trees, can provide information for understanding the structure of the vegetable community. Considering the environmental factors can influence the spatial pattern of occurrences of species in native forests and related attributes, describing relationship between environmental characteristics and spatial pattern of forest species can be associated with the dynamics of forests. The objective of the present study is to assess different statistical methods used to identify which soil attributes are associated with the basal area of each tree selected species. The basal area was considered as the response variable and the covariates are given by a large number of physical and chemical attributes of the soil, measured at a grid of locations covering the study area. The methods considered are the multiple linear regression with stepwise model selection, generalized additive models and regression trees. Spatial effects were added to the models, in order to ascertain whether there is residual spatial patterns not captured by the covariates. Thus, simultaneous autoregressive model, autoregressive conditional and geostatistical were considered. Considering the large number of soil attributes, analysis were were conducted both ways, using the original covariates, and using factors identified in a preliminar factor analysis of the soil attributes. Model selection was used to identify the relevant attributes of soil as well as the presence and better description of spatial patterns. The study area was the Ecological Station of Assis, the Conservation Unit of the State of São Paulo in permanent plots within the \"Diversity Dynamics and Conservation Forests in the State of São Paulo: 40 ha of permanent plots\" project, under the research project FAPESP biota. The analyzes reported here refer to the basal area of the species Copaifera langsdorffii, Vochysia tucanorum and Xylopia aromatica. Results differ among the considered methods reinforcing the reccomendation of considering differing modeling strategies. Covariates consistently associated with basal area are slope, altitude and aluminum saturation, potassium, relevant to at least two of the species. Results obtained showed the presence of patterns in residual variability, even taking into account the effects of covariates. The soil characteristics only partially explain the variability of the basal area and there are spatial patterns not captured by these covariates.
7

Edberg, Christopher, and Oliver Kjellander. "Calendar Anomalies in the Nordic Stock Markets : A quantitative study of the Sell in May effect, January effect & Monthly Anomalies." Thesis, Linnéuniversitetet, Institutionen för ekonomistyrning och logistik (ELO), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105272.

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This study has applied a geographical perspective with the ambition of evaluating the presence of the Sell in May effect, January effect and monthly anomalies in the Nordic stock markets. In extension the study examines the relationship between corporate size and the returns of calendar anomalies. The study has conducted statistical tests based on Newey-West regressions as well as a Generalized Auto-Regressive Conditional Heteroscedasticity model. The findings suggest that the Sell in May and January are present in the Nordic region and partially abide by theory and results of previous research. The findings suggest that the Sell in May and January effect are independent, however, tendencies when the January effect has a considerable influence on the Sell in May effect are also evident. Additionally, the “April Effect” is an unexpected outlier with positive excess returns that was identified through this study.
8

Arotiba, Gbenga Joseph. "Pricing American Style Employee Stock Options having GARCH Effects." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_3057_1298615964.

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We investigate some simulation-based approaches for the valuing of the employee stock options. The mathematical models that deal with valuation of such options include the work of Jennergren and Naeslund [L.P Jennergren and B. Naeslund, A comment on valuation of executive stock options and the FASB proposal, Accounting Review 68 (1993) 179-183]. They used the Black and Scholes [F. Black and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy 81(1973) 637-659] and extended partial differential equation for an option that includes the early exercise. Some other major relevant works to this mini thesis are Hemmer et al. [T Hemmer, S. Matsunaga and T Shevlin, The influence of risk diversification on the early exercise of employee stock options by executive officers, Journal of Accounting and Economics 21(1) (1996) 45-68] and Baril et al. [C. Baril, L. Betancourt, J. Briggs, Valuing employee stock options under SFAS 123 R using the Black-Scholes-Merton and lattice model approaches, Journal of Accounting Education 25 (1-2) (2007) 88-101]. The underlying assets are studied under the GARCH (generalized autoregressive conditional heteroskedasticity) effects. Particular emphasis is made on the American style employee stock options.

9

Santos, Helton Saulo Bezerra dos. "Essays on Birnbaum-Saunders models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/87375.

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Nessa tese apresentamos três diferentes aplicações dos modelos Birnbaum-Saunders. No capítulo 2 introduzimos um novo método por função-núcleo não-paramétrico para a estimação de densidades assimétricas, baseado nas distribuições Birnbaum-Saunders generalizadas assimétricas. Funções-núcleo baseadas nessas distribuições têm a vantagem de fornecer flexibilidade nos níveis de assimetria e curtose. Em adição, os estimadores da densidade por função-núcleo Birnbaum-Saunders gene-ralizadas assimétricas são livres de viés na fronteira e alcançam a taxa ótima de convergência para o erro quadrático integrado médio dos estimadores por função-núcleo-assimétricas-não-negativos da densidade. Realizamos uma análise de dados consistindo de duas partes. Primeiro, conduzimos uma simulação de Monte Carlo para avaliar o desempenho do método proposto. Segundo, usamos esse método para estimar a densidade de três dados reais da concentração de poluentes atmosféricos. Os resultados numéricos favorecem os estimadores não-paramétricos propostos. No capítulo 3 propomos uma nova família de modelos autorregressivos de duração condicional baseados nas distribuições misturas de escala Birnbaum-Saunders (SBS). A distribuição Birnbaum-Saunders (BS) é um modelo que tem recebido considerável atenção recentemente devido às suas boas propriedades. Uma extensão dessa distribuição é a classe de distribuições SBS, a qual (i) herda várias das boas propriedades da distribuição BS, (ii) permite a estimação de máxima verossimilhança em uma forma eficiente usando o algoritmo EM, e (iii) possibilita a obtenção de um procedimento de estimação robusta, entre outras propriedades. O modelo autorregressivo de duração condicional é a família primária de modelos para analisar dados de duração de transações de alta frequência. A metodologia estudada aqui inclui estimação dos parâmetros pelo algoritmo EM, inferência para esses parâmetros, modelo preditivo e uma análise residual. Realizamos simulações de Monte Carlo para avaliar o desempenho da metodologia proposta. Ainda, avalia-mos a utilidade prática dessa metodologia usando dados reais de transações financeiras da bolsa de valores de Nova Iorque. O capítulo 4 trata de índices de capacidade do processo (PCIs), os quais são ferramentas utilizadas pelas empresas para determinar a qualidade de um produto e avaliar o desempenho de seus processos de produção. Estes índices foram desenvolvidos para processos cuja característica de qualidade tem uma distribuição normal. Na prática, muitas destas ca-racterísticas não seguem esta distribuição. Nesse caso, os PCIs devem ser modificados considerando a não-normalidade. O uso de PCIs não-modificados podemlevar a resultados inadequados. De maneira a estabelecer políticas de qualidade para resolver essa inadequação, transformação dos dados tem sido proposta, bem como o uso de quantis de distribuições não-normais. Um distribuição não-normal assimétrica o qual tem tornado muito popular em tempos recentes é a distribuição Birnbaum-Saunders (BS). Propomos, desenvolvemos, implementamos e aplicamos uma metodologia baseada em PCIs para a distribuição BS. Além disso, realizamos um estudo de simulação para avaliar o desempenho da metodologia proposta. Essa metodologia foi implementada usando o software estatístico chamado R. Aplicamos essa metodologia para um conjunto de dados reais de maneira a ilustrar a sua flexibilidade e potencialidade.
In this thesis, we present three different applications of Birnbaum-Saunders models. In Chapter 2, we introduce a new nonparametric kernel method for estimating asymmetric densities based on generalized skew-Birnbaum-Saunders distributions. Kernels based on these distributions have the advantage of providing flexibility in the asymmetry and kurtosis levels. In addition, the generalized skew-Birnbaum-Saunders kernel density estimators are boundary bias free and achieve the optimal rate of convergence for the mean integrated squared error of the nonnegative asymmetric kernel density estimators. We carry out a data analysis consisting of two parts. First, we conduct a Monte Carlo simulation study for evaluating the performance of the proposed method. Second, we use this method for estimating the density of three real air pollutant concentration data sets, whose numerical results favor the proposed nonparametric estimators. In Chapter 3, we propose a new family of autoregressive conditional duration models based on scale-mixture Birnbaum-Saunders (SBS) distributions. The Birnbaum-Saunders (BS) distribution is a model that has received considerable attention recently due to its good properties. An extension of this distribution is the class of SBS distributions, which allows (i) several of its good properties to be inherited; (ii) maximum likelihood estimation to be efficiently formulated via the EM algorithm; (iii) a robust estimation procedure to be obtained; among other properties. The autoregressive conditional duration model is the primary family of models to analyze high-frequency financial transaction data. This methodology includes parameter estimation by the EM algorithm, inference for these parameters, the predictive model and a residual analysis. We carry out a Monte Carlo simulation study to evaluate the performance of the proposed methodology. In addition, we assess the practical usefulness of this methodology by using real data of financial transactions from the New York stock exchange. Chapter 4 deals with process capability indices (PCIs), which are tools widely used by companies to determine the quality of a product and the performance of their production processes. These indices were developed for processes whose quality characteristic has a normal distribution. In practice, many of these characteristics do not follow this distribution. In such a case, the PCIs must be modified considering the non-normality. The use of unmodified PCIs can lead to inadequacy results. In order to establish quality policies to solve this inadequacy, data transformation has been proposed, as well as the use of quantiles from non-normal distributions. An asymmetric non-normal distribution which has become very popular in recent times is the Birnbaum-Saunders (BS) distribution. We propose, develop, implement and apply a methodology based on PCIs for the BS distribution. Furthermore, we carry out a simulation study to evaluate the performance of the proposed methodology. This methodology has been implemented in a noncommercial and open source statistical software called R. We apply this methodology to a real data set to illustrate its flexibility and potentiality.
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"The impact of exchange rate volatility on emerging market exports : a comparative study." Thesis, 2013. http://hdl.handle.net/10210/8334.

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M.Com. (Economic Development and Policy Issues)
This research analyses the effect of exchange rate volatility on exports using a sample of nine emerging countries – Argentina, Brazil, India, Indonesia, Mexico, Malaysia, Poland, South Africa and Thailand – between 1995 and 2010. The study uses panel data models, with a standard exports equation with exports performance determined by exchange rate volatility, the level of exchange rate, demand conditions in major countries as well as terms of trade. Exchange rate volatility is measured by Generalised Autoregressive Conditional Heteroscedasticity (GARCH) and conventional standard deviation in order to determine if the instrument of volatility used influences the nature of the relationship between exchange rate volatility and exports. The results show that exchange rate volatility has a significant negative effect on the performance of exports regardless of the measure of volatility used. The Pedroni residual cointegration method was used to test for panel cointegration to determine if there is a long-run relationship among the variables, and the test showed that a long-run relationship does exists. Generally, the study concludes that policy mix that will reduce exchange rate volatility (such as managed exchange rate regimes) and relatively competitive exchange rates are essential for emerging markets in order to sustain their exports performance.
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Книги з теми "Generalized Autoregressive Conditional Heteroscedasticity":

1

Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. Cambridge, MA: National Bureau of Economic Research, 1994.

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2

Kodaira, Ryoichi. Autoregressive conditional heteroscedasticity in the Japanese short-term money market rates (Gensaki rates). [s.l.]: typescript, 1994.

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3

Shi, Feng. Learn About the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in R With Data From the DJIA 30 Stock Time Series (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526487650.

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4

Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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Krause, Timothy A. Pricing of Futures Contracts. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0015.

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This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.

Частини книг з теми "Generalized Autoregressive Conditional Heteroscedasticity":

1

Chang, Bao Rong. "Novel Prediction Approach – Quantum-Minimum Adaptation to ANFIS Outputs and Nonlinear Generalized Autoregressive Conditional Heteroscedasticity." In Fuzzy Systems and Knowledge Discovery, 908–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_113.

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2

Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. "Autoregressive Conditional Heteroscedasticity." In Introduction to Modern Time Series Analysis, 281–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33436-8_8.

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3

Alzghool, Raed. "ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93726.

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This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditional variance of process is unknown. The AQL estimation substitutes the variance and covariance by kernel estimation in QL. Reports of simulation outcomes, numerical cases, and applications of the methods to daily exchange rate series and weekly prices’ changes of crude oil are presented.
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Yu, Philip L. H., Edmond H. C. Wu, and W. K. Li. "Financial Data Mining Using Flexible ICA-GARCH Models." In Dynamic and Advanced Data Mining for Progressing Technological Development, 255–72. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-908-3.ch011.

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As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.
5

Geweke, John. "Exact inference in models with autoregressive conditional heteroscedasticity." In Dynamic Econometric Modeling, 73–104. Cambridge University Press, 1988. http://dx.doi.org/10.1017/cbo9780511664342.006.

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Bond, Shaun A. "A review of asymmetric conditional density functions in autoregressive conditional heteroscedasticity models." In Return Distributions in Finance, 21–46. Elsevier, 2001. http://dx.doi.org/10.1016/b978-075064751-9.50003-5.

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7

Mills, Terence C. "Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes." In Applied Time Series Analysis, 161–71. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-813117-6.00010-7.

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8

"6. Conditional Heteroscedasticity: Nonlinear Autoregressive Models, ARCH Models, Stochastic Volatility Models." In Financial Econometrics, 117–50. Princeton University Press, 2002. http://dx.doi.org/10.1515/9780691187020-007.

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9

Mugaloglu, Yusuf I. "The Effect of Index Warrant Trading on the Underlying Volatility in the Post-Crisis Period." In Technology and Financial Crisis, 195–208. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3006-2.ch017.

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The global financial crisis of 2007-2008 led to a sharp decrease in asset prices and increased volatility in financial markets. Before the crisis, warrant trading was often justified by assuming a more stabilised complete market and lower volatility. The Istanbul Stock Exchange introduced a warrant market and trading of ISE-30 index-based warrants in 2010. The chapter examines the impact of index-based warrant trading on the volatility of underlying ISE-30 index during post-crisis period of 2009-2011. The study employed a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approach. In order to scrutinize the influence of index warrant trading on the volatility of underlying, two GARCH (1,1) models were specified; one included the volume of index warrants in the conditional mean equation and the other included a dummy variable in the conditional variance equation. The results show that index warrant trading did not lead to lower underlying volatility over the post-crisis period.
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"Estimating Long-Term Volatility on National Stock Exchange of India." In Emerging Research on Monetary Policy, Banking, and Financial Markets, 229–37. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9269-3.ch011.

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The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

Тези доповідей конференцій з теми "Generalized Autoregressive Conditional Heteroscedasticity":

1

Chi Xie and Lin Yao. "Portfolio Value-at-Risk estimating on markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model." In 2012 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2012. http://dx.doi.org/10.1109/iciii.2012.6339654.

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2

Czech, Katarzyna. "Is a Japanese yen a safe haven? Relationship between Japanese currency and financial market uncertainty." In 3rd International Conference on Administrative & Financial Sciences. Cihan University - Erbil, 2021. http://dx.doi.org/10.24086/afs2020/paper.353.

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Japan's low-interest rates made the country's currency the primary funding currency in carry trade speculative strategies. Investors' activity in carry trade strategies has an enormous impact on the foreign exchange market volatility. A large inflow of capital to countries with higher interest rates contributes to their currency appreciation, and, in turn, a large outflow of capital from countries with a low-interest rate leads to a significant depreciation of their currency. However, in times of crisis and high uncertainty in the financial markets, investors massively withdraw from the carry trade. They sell financial assets purchased in a country with higher interest rates and then repay loans taken in a country with low-interest rates. A sudden increase in the supply of a country's currency with higher interest rates leads to its depreciation. On the other hand, the rise in demand for a country's currency with low-interest rates leads to its appreciation. The Japanese yen is one of the most popular funding currency in the carry trade and thus tends to appreciate during crisis periods. The paper aims to investigate the relationship between Japanese yen value and financial market uncertainty measured by the Volatility Index VIX and St. Louis FED Financial Stress Index. Based on the component generalized autoregressive conditional heteroscedasticity model CGARCH with asymmetric threshold term, it has been shown that the increase in financial markets uncertainty contributes to significant appreciation of the Japanese yen against the US dollar. It implies that the Japanese currency is an example of a safe-haven currency and can be applied to hedge financial stress for global equity investors.
3

Chen, Hao, Jie Wu, and Shan Gao. "A Study of Autoregressive Conditional Heteroscedasticity Model in Load Forecasting." In 2006 International Conference on Power System Technology. IEEE, 2006. http://dx.doi.org/10.1109/icpst.2006.321620.

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4

Sin, Kuek Jia, Chin Wen Cheong, and Tan Siow Hooi. "Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980990.

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5

Ou, ChengQi, Charlene Xie, Jun Xu, and YunLiang Hu. "Generalized Autoregressive Conditional Heteroskedasticity in Credit Risk Measurement." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304395.

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6

Ilbeigi, Mohammad, Alireza Joukar, and Baabak Ashuri. "Modeling and Forecasting the Price of Asphalt Cement Using Generalized Auto Regressive Conditional Heteroscedasticity." In Construction Research Congress 2016. Reston, VA: American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479827.071.

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7

Li, Qianru, Christophe Tricaud, Rongtao Sun, and YangQuan Chen. "Great Salt Lake Surface Level Forecasting Using FIGARCH Model." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34909.

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedasticity should be included in time series with high volatility.
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Ranjan, Nikhil, Hema A. Murthy, and Timothy A. Gonsalves. "Detection of SYN flooding attacks using generalized autoregressive conditional heteroskedasticity (GARCH) modeling technique." In 2010 National Conference On Communications (NCC). IEEE, 2010. http://dx.doi.org/10.1109/ncc.2010.5430151.

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9

Wang, Y., M. Sznaier, O. Camps, and F. Pait. "Identification of a class of generalized autoregressive conditional heteroskedasticity (GARCH) models with applications to covariance propagation." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402327.

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10

Dias, Rui, Paula Heliodoro, Paulo Alexandre, and Cristina Vasco. "THE SHOCKS BETWEEN OIL MARKET TO THE BRIC STOCK MARKETS: A GENERALIZED VAR APPROACH." In 4th International Scientific Conference – EMAN 2020 – Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2020. http://dx.doi.org/10.31410/eman.2020.25.

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The pandemic (Covid-19) has affected the global economy, and the impact on financial markets seems inevitable. In view of these events, this essay aims to analyse the shocks between the stock market indices of Brazil (BOVESPA), China (SSEC) India (SENSEX), Russia (IMOEX) and oil (WTC), in the period from January 2, 2019 to May 29, 2020. In order to carry out this analysis, different approaches were undertaken with a view to gauging whether (i) the global pandemic has accentuated the shocks between the BRIC financial markets and the WTC? The daily yields do not have normal distributions, show negative asymmetries, leptokurtic, and exhibit conditional heteroscedasticity. In general, we find evidence that the WTC causes the markets of Russia and India, China does not cause any market, and Brazil is not caused by any market analysed. On the other hand, short-term market shocks are relevant and create some arbitrage opportunities. However, our study did not analyse anomalous returns in these financial markets. These findings also open space for market regulators to take action to ensure better information between international financial markets.

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